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An image segmentation method based on Simple Linear Iterative Clustering and graph-based semi-supervised learning

机译:一种基于简单线性迭代聚类和基于图的半监督学习的图像分割方法

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Image segmentation is one of the most popular applications in contemporary computer vision and image processing field. In this paper, a novel segmentation framework is proposed based on Simple Linear Iterative Clustering (SLIC) and graph-based semi-supervised learning (graph-based SSL). With the SLIC approach utilized in the first stage, we get the anchor points which are SLIC clustering centers. Graph-based SSL method using anchor points and labeled samples are used to calculate category of anchor points, through which, the category of sample points is further determined. Experimental results show the effectiveness and robustness of the proposed method.
机译:图像分割是当代计算机视觉和图像处理领域中最受欢迎的应用之一。本文基于简单的线性迭代聚类(SLIC)和基于图形的半监督学习(基于图形的SSL),提出了一种新的分割框架。利用第一阶段中使用的SLIC方法,我们得到了锚点,即切片聚类中心。使用锚点和标记样本的基于图的SSL方法用于计算锚点的类别,通过此进一步确定采样点的类别。实验结果表明了该方法的有效性和鲁棒性。

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